{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "81e97078",
   "metadata": {},
   "source": [
    "# SuperKnowa Model Evaluation on CoQA Q&A Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "8372f3e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import glob\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5e9bc8d9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "result_1k\n"
     ]
    }
   ],
   "source": [
    "cd result_1k\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "52f649c3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SuperKnowa_Coqa_Llama.csv   SuperKnowa_Coqa_flanT5.csv\r\n",
      "SuperKnowa_Coqa_bloom.csv\r\n"
     ]
    }
   ],
   "source": [
    "ls"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "56be69f4",
   "metadata": {},
   "source": [
    "Model Used for evaluation with 1000 Q&A from CoQA dataset\n",
    "- Bloom\n",
    "- FlanT5\n",
    "- Llama (Pre-trained)\n",
    "\n",
    "Evaluation Matrix used\n",
    "\n",
    "- blue score\n",
    "- meteor score\n",
    "- rouge score\n",
    "- SentenceSim score\n",
    "- SimHash score\n",
    "- perplexity score\n",
    "- bleurt score\n",
    "- F1 score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "d4f16561",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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8/PzC24IFC8pROQAAAAAAVdkOs51LSeTk5KTdT6VSGW3l7ZObmxu5ubllLxIAAAAAgO+NKrESvWXLlhERGSvIlyxZkrHSfOt+pe0DAAAAAABbVIkQvUOHDtGyZct4+eWXC9s2bNgQ48ePj759+yb269OnT1qfiIiXXnqp2D4AAAAAALDFDrOdy9q1a2POnDmF9+fOnRvTp0+Pxo0bR9u2beOyyy6Lm2++OfbYY4/YY4894uabb466devG6aefXthn8ODBseuuu8bw4cMjIuLSSy+Nww47LP74xz/GCSecEP/+97/jlVdeiTfffLPSzw8AAAAAgKpnhwnRp06dGocffnjh/SFDhkRExJlnnhljxoyJq666Kr755pu48MILY+XKlXHQQQfFSy+9FA0aNCjsM3/+/KhW7f+/uL5v377x6KOPxnXXXRdDhw6NTp06xWOPPRYHHXRQ5Z0YAAAAAABV1g4Tovfv3z9SqVTi4zk5OTFs2LAYNmxY4jHjxo3LaDvllFPilFNOqYAKAQAAAADY2VSJPdEBAAAAACAbhOgAAAAAAJBAiA4AAAAAAAmE6AAAAAAAkECIDgAAAAAACYToAAAAAACQQIgOAAAAAAAJhOgAAAAAAJBAiA4AAAAAAAmE6AAAAAAAkECIDgAAAAAACYToAAAAAACQQIgOAAAAAAAJhOgAAAAAAJBAiA4AAAAAAAmE6AAAAAAAkECIDgAAAAAACYToAAAAAACQQIgOAAAAAAAJhOgAAAAAAJBAiA4AAAAAAAmE6AAAAAAAkECIDgAAAAAACYToAAAAAACQQIgOAAAAAAAJhOgAAAAAAJBAiA4AAAAAAAmE6AAAAAAAkECIDgAAAAAACYToAAAAAACQQIgOAAAAAAAJhOgAAAAAAJBAiA4AAAAAAAmE6AAAAAAAkECIDgAAAAAACYToAAAAAACQQIgOAAAAAAAJhOgAAAAAAJBAiA4AAAAAAAmE6AAAAAAAkECIDgAAAAAACYToAAAAAACQQIgOAAAAAAAJhOgAAAAAAJBAiA4AAAAAAAmE6AAAAAAAkECIDgAAAAAACYToAAAAAACQQIgOAAAAAAAJhOgAAAAAAJBAiA4AAAAAAAmE6AAAAAAAkECIDgAAAAAACYToAAAAAACQQIgOAAAAAAAJhOgAAAAAAJBAiA4AAAAAAAmE6AAAAAAAkECIDgAAAAAACYToAAAAAACQQIgOAAAAAAAJhOgAAAAAAJBAiA4AAAAAAAmE6AAAAAAAkECIDgAAAAAACapMiN6+ffvIycnJuP3qV78q8vhx48YVefzHH39cyZUDAAAAAFBV1ch2ASU1ZcqU2Lx5c+H9Dz74II444oj48Y9/XGy/WbNmRcOGDQvvN2vWbLvVCAAAAADA90uVCdG3Dr//8Ic/RKdOnaJfv37F9mvevHk0atSoxPOsX78+1q9fX3h/9erVpaoTAAAAAIDvjyqznct3bdiwIR588ME455xzIicnp9hjDzjggGjVqlUMGDAgXn/99W2OPXz48MjLyyu8tWnTpqLKBgAAAACgiqmSIfpTTz0Vq1atirPOOivxmFatWsU999wTTzzxRIwdOzY6d+4cAwYMiAkTJhQ79jXXXBP5+fmFtwULFlRw9QAAAAAAVBVVZjuX7xo5cmQcffTR0bp168RjOnfuHJ07dy6836dPn1iwYEGMGDEiDjvssMR+ubm5kZubW6H1AgAAAABQNVW5leiff/55vPLKK3HuueeWum/v3r1j9uzZ26EqAAAAAAC+j6pciD569Oho3rx5HHvssaXu+95770WrVq22Q1UAAAAAAHwfVantXAoKCmL06NFx5plnRo0a6aVfc801sXDhwnjggQciIuLWW2+N9u3bR9euXQsvRPrEE0/EE088kY3SAQAAAACogqpUiP7KK6/E/Pnz45xzzsl4bNGiRTF//vzC+xs2bIgrr7wyFi5cGHXq1ImuXbvGs88+G8ccc0xllgwAAAAAQBVWpUL0gQMHRiqVKvKxMWPGpN2/6qqr4qqrrqqEqgAAAAAA+L6qcnuiAwAAAABAZRGiAwAAAABAAiE6AAAAAAAkEKIDAAAAAEACIToAAAAAACQQogMAAAAAQAIhOgAAAAAAJBCiAwAAAABAAiE6AAAAAAAkEKIDAAAAAEACIToAAAAAACQQogMAAAAAQAIhOgAAAAAAJBCiAwAAAABAAiE6AAAAAAAkEKIDAAAAAEACIToAAAAAACQQogMAAAAAQAIhOgAAAAAAJBCiAwAAAABAAiE6AAAAAAAkEKIDAAAAAEACIToAAAAAACQQogMAAAAAQAIhOgAAAAAAJBCiAwAAAABAAiE6AAAAAAAkEKIDAAAAAEACIToAAAAAACQQogMAAAAAQAIhOgAAAAAAJBCiAwAAAABAAiE6AAAAAAAkEKIDAAAAAEACIToAAAAAACQQogMAAAAAQAIhOgAAAAAAJBCiAwAAAABAAiE6AAAAAAAkEKIDAAAAAEACIToAAAAAACQQogMAAAAAQAIhOgAAAAAAJBCiAwAAAABAAiE6AAAAAAAkEKIDAAAAAEACIToAAAAAACQQogMAAAAAQAIhOgAAAAAAJBCiAwAAAABAAiE6AAAAAAAkEKIDAAAAAEACIToAAAAAACQQogMAAAAAQAIhOgAAAAAAJBCiAwAAAABAAiE6AAAAAAAkEKIDAAAAAEACIToAAAAAACQQogMAAAAAQAIhOgAAAAAAJBCiAwAAAABAAiE6AAAAAAAkEKIDAAAAAEACIToAAAAAACSoMiH6sGHDIicnJ+3WsmXLYvuMHz8+unfvHrVr146OHTvGXXfdVUnVAgAAAADwfVAj2wWURteuXeOVV14pvF+9evXEY+fOnRvHHHNMnHfeefHggw/GW2+9FRdeeGE0a9YsBg0aVBnlAgAAAABQxVWpEL1GjRrbXH2+xV133RVt27aNW2+9NSIi9t5775g6dWqMGDFCiA4AAAAAQIlUme1cIiJmz54drVu3jg4dOsRPf/rT+OyzzxKPnTRpUgwcODCt7cgjj4ypU6fGxo0bE/utX78+Vq9enXYDAAAAAGDnVGVC9IMOOigeeOCBePHFF+Pee++NxYsXR9++fWP58uVFHr948eJo0aJFWluLFi1i06ZNsWzZssR5hg8fHnl5eYW3Nm3aVOh5AAAAAABQdVSZEP3oo4+OQYMGxT777BM//OEP49lnn42IiPvvvz+xT05OTtr9VCpVZPt3XXPNNZGfn194W7BgQQVUDwAAAABAVVSl9kT/rnr16sU+++wTs2fPLvLxli1bxuLFi9PalixZEjVq1IgmTZokjpubmxu5ubkVWisAAAAAAFVTlVmJvrX169fHRx99FK1atSry8T59+sTLL7+c1vbSSy9Fjx49ombNmpVRIgAAAAAAVVyVCdGvvPLKGD9+fMydOzfefvvtOOWUU2L16tVx5plnRsR/t2EZPHhw4fEXXHBBfP755zFkyJD46KOPYtSoUTFy5Mi48sors3UKAAAAAABUMVVmO5cvvvgiTjvttFi2bFk0a9YsevfuHZMnT4527dpFRMSiRYti/vz5hcd36NAhnnvuubj88svjb3/7W7Ru3Tpuu+22GDRoULZOAQAAAACAKqbKhOiPPvposY+PGTMmo61fv37x7rvvbqeKAAAAAAD4vqsy27kAAAAAAEBlE6IDAAAAAEACIToAAAAAACQQogMAAAAAQAIhOgAAAAAAJBCiAwAAAABAAiE6AAAAAAAkEKIDAAAAAEACIToAAAAAACQQogMAAAAAQAIhOgAAAAAAJBCiAwAAAABAAiE6AAAAAAAkEKIDAAAAAEACIToAAAAAACQQogMAAAAAQAIhOgAAAAAAJBCiAwAAAABAAiE6AAAAAAAkEKIDAAAAAEACIToAAAAAACQQogMAAAAAQAIhOgAAAAAAJBCiAwAAAABAAiE6AAAAAAAkEKIDAAAAAEACIToAAAAAACQQogMAAAAAQAIhOgAAAAAAJBCiAwAAAABAAiE6AAAAAAAkEKIDAAAAAEACIToAAAAAACQQogMAAAAAQAIhOgAAAAAAJBCiAwAAAABAAiE6AAAAAAAkEKIDAAAAAEACIToAAAAAACQQogMAAAAAQAIhOgAAAAAAJBCiAwAAAABAAiE6AAAAAAAkEKIDAAAAAEACIToAAAAAACQQogMAAAAAQAIhOgAAAAAAJBCiAwAAAABAAiE6AAAAAAAkEKIDAAAAAEACIToAAAAAACQQogMAAAAAQAIhOgAAAAAAJBCiAwAAAABAAiE6AAAAAAAkEKIDAAAAAEACIToAAAAAACQQogMAAAAAQAIhOgAAAAAAJBCiAwAAAABAAiE6AAAAAAAkEKIDAAAAAEACIToAAAAAACQQogMAAAAAQAIhOgAAAAAAJKgyIfrw4cOjZ8+e0aBBg2jevHmceOKJMWvWrGL7jBs3LnJycjJuH3/8cSVVDQAAAABAVVZlQvTx48fHr371q5g8eXK8/PLLsWnTphg4cGB8/fXX2+w7a9asWLRoUeFtjz32qISKAQAAAACo6mpku4CSeuGFF9Lujx49Opo3bx7Tpk2Lww47rNi+zZs3j0aNGm3H6gAAAAAA+D6qMivRt5afnx8REY0bN97msQcccEC0atUqBgwYEK+//nqxx65fvz5Wr16ddgMAAAAAYOdUJUP0VCoVQ4YMiUMOOSS6deuWeFyrVq3innvuiSeeeCLGjh0bnTt3jgEDBsSECRMS+wwfPjzy8vIKb23atNkepwAAAAAAQBVQZbZz+a6LLroo3n///XjzzTeLPa5z587RuXPnwvt9+vSJBQsWxIgRIxK3gLnmmmtiyJAhhfdXr14tSAcAAAAA2ElVuZXoF198cTz99NPx+uuvx2677Vbq/r17947Zs2cnPp6bmxsNGzZMuwEAAAAAsHOqMivRU6lUXHzxxfHkk0/GuHHjokOHDmUa57333otWrVpVcHUAAAAAAHwfVZkQ/Ve/+lU8/PDD8e9//zsaNGgQixcvjoiIvLy8qFOnTkT8dyuWhQsXxgMPPBAREbfeemu0b98+unbtGhs2bIgHH3wwnnjiiXjiiSeydh4AAAAAAFQdVSZE//vf/x4REf37909rHz16dJx11lkREbFo0aKYP39+4WMbNmyIK6+8MhYuXBh16tSJrl27xrPPPhvHHHNMZZUNAAAAAEAVVmVC9FQqtc1jxowZk3b/qquuiquuumo7VQQAAAAAwPddlbuwKAAAAAAAVBYhOgAAAAAAJBCiAwAAAABAAiE6AAAAAAAkEKIDAAAAAEACIToAAAAAACQQogMAAAAAQAIhOgAAAAAAJBCiAwAAAABAAiE6AAAAAAAkEKIDAAAAAEACIToAAAAAACQQogMAAAAAQAIhOgAAAAAAJBCiAwAAAABAAiE6AAAAAAAkEKIDAAAAAEACIToAAAAAACQQogMAAAAAQAIhOgAAAAAAJBCiAwAAAABAAiE6AAAAAAAkEKIDAAAAAEACIToAAAAAACQQogMAAAAAQAIhOgAAAAAAJBCiAwAAAABAAiE6AAAAAAAkEKIDAAAAAEACIToAAAAAACQQogMAAAAAQAIhOgAAAAAAJBCiAwAAAABAAiE6AAAAAAAkEKIDAAAAAEACIToAAAAAACQQogMAAAAAQAIhOgAAAAAAJBCiAwAAAABAAiE6AAAAAAAkEKIDAAAAAEACIToAAAAAACQQogMAAAAAQAIhOgAAAAAAJBCiAwAAAABAAiE6AAAAAAAkEKIDAAAAAEACIToAAAAAACQQogMAAAAAQAIhOgAAAAAAJBCiAwAAAABAAiE6AAAAAAAkEKIDAAAAAEACIToAAAAAACQQogMAAAAAQAIhOgAAAAAAJBCiAwAAAABAAiE6AAAAAAAkEKIDAAAAAEACIToAAAAAACQQogMAAAAAQAIhOgAAAAAAJBCiAwAAAABAAiE6AAAAAAAkEKIDAAAAAEACIToAAAAAACSociH6nXfeGR06dIjatWtH9+7d44033ij2+PHjx0f37t2jdu3a0bFjx7jrrrsqqVIAAAAAAKq6KhWiP/bYY3HZZZfFtddeG++9914ceuihcfTRR8f8+fOLPH7u3LlxzDHHxKGHHhrvvfde/Pa3v41LLrkknnjiiUquHAAAAACAqqhGtgsojVtuuSV+8YtfxLnnnhsREbfeemu8+OKL8fe//z2GDx+ecfxdd90Vbdu2jVtvvTUiIvbee++YOnVqjBgxIgYNGlTkHOvXr4/169cX3s/Pz4+IiNWrV1fw2VQdBevXZbsEAEpoZ36/ygbvkQBVh/fIyuU9EqDq2JnfI7eceyqVKva4KhOib9iwIaZNmxZXX311WvvAgQNj4sSJRfaZNGlSDBw4MK3tyCOPjJEjR8bGjRujZs2aGX2GDx8eN954Y0Z7mzZtylE9AFSOvFuzXQEA7Ji8RwJA0bxHRqxZsyby8vISH68yIfqyZcti8+bN0aJFi7T2Fi1axOLFi4vss3jx4iKP37RpUyxbtixatWqV0eeaa66JIUOGFN4vKCiIFStWRJMmTSInJ6cCzgTYEaxevTratGkTCxYsiIYNG2a7HADYYXiPBIBM3h/h+ymVSsWaNWuidevWxR5XZUL0LbYOslOpVLHhdlHHF9W+RW5ubuTm5qa1NWrUqAyVAlVBw4YN/R8gACiC90gAyOT9Eb5/iluBvkWVubBo06ZNo3r16hmrzpcsWZKx2nyLli1bFnl8jRo1okmTJtutVgAAAAAAvh+qTIheq1at6N69e7z88stp7S+//HL07du3yD59+vTJOP6ll16KHj16FLkfOgAAAAAAfFeVCdEjIoYMGRL33XdfjBo1Kj766KO4/PLLY/78+XHBBRdExH/3Mx88eHDh8RdccEF8/vnnMWTIkPjoo49i1KhRMXLkyLjyyiuzdQrADiI3NzduuOGGjO2bAGBn5z0SADJ5f4SdW05qyybhVcSdd94Zf/rTn2LRokXRrVu3+Mtf/hKHHXZYREScddZZMW/evBg3blzh8ePHj4/LL788Zs6cGa1bt47f/OY3haE7AAAAAAAUp8qF6AAAAAAAUFmq1HYuAAAAAABQmYToAAAAAACQQIgOAAAAAAAJhOgAAAAAAJBAiA4AAAAAAAmE6AAAAAAAkKBGtgsAyIZvvvkmNm7cmNbWsGHDLFUDADuOtWvXRkFBQVqb90gAdmYbN26MZ599NmbPnh2tWrWKk046KerVq5ftsoBKlJNKpVLZLgKgMqxbty6uuuqqePzxx2P58uUZj2/evDkLVQFA9s2dOzcuuuiiGDduXHz77beF7alUKnJycrxHArBT6du3bzz33HPRqFGjWLp0aQwYMCBmzZoV7dq1iwULFkTz5s1j4sSJseuuu2a7VKCSWIkO7DR+/etfx+uvvx533nlnDB48OP72t7/FwoUL4+67744//OEP2S4PALLmZz/7WUREjBo1Klq0aBE5OTlZrggAsmfy5MmxYcOGiIi49tpro3r16vH5559Hy5YtY/ny5XH88cfH9ddfHyNHjsxypUBlsRId2Gm0bds2Hnjggejfv380bNgw3n333dh9993jH//4RzzyyCPx3HPPZbtEAMiK+vXrx7Rp06Jz587ZLgUAsq5atWqxePHiaN68eXTu3DluueWWOPbYYwsfHzduXJx99tkxd+7cLFYJVCYXFgV2GitWrIgOHTpExH/3dl2xYkVERBxyyCExYcKEbJYGAFnVs2fPWLBgQbbLAIAdxpZvZa1atarw35FbdOjQIRYtWpSNsoAssZ0LsNPo2LFjzJs3L9q1axddunSJxx9/PHr16hXPPPNMNGrUKNvlAUDW3HfffXHBBRfEwoULo1u3blGzZs20x/fdd98sVQYA2XHWWWdFbm5ubNy4MT7//PPo0qVL4WOLFi3yb0jYyQjRgZ3G2WefHTNmzIh+/frFNddcE8cee2zcfvvtsWnTprjllluyXR4AZM3SpUvj008/jbPPPruwLScnx4VFAdgpDR48uHAl+gknnBBr165Ne/yJJ56I/fffPwuVAdliT3RgpzV//vyYOnVqdOrUKfbbb79slwMAWdOlS5fYe++946qrrirywqLt2rXLUmUAsOP5+uuvo3r16lG7du1slwJUEiE6AADs5OrVqxczZsyI3XffPdulAEDWdezYMaZMmRJNmjTJdinADsJ2LsBO5Z133olx48bFkiVLoqCgIO0xW7oAsLP6wQ9+IEQHgP/PvHnzbGUGpBGiAzuNm2++Oa677rro3LlzxlfVt/7aOgDsTH70ox/F5ZdfHv/5z39in332ybiw6PHHH5+lygAAIPts5wLsNFq0aBF//OMf46yzzsp2KQCwQ6lWrVriYy4sCsDOplq1avHaa69F48aNiz1u3333raSKgGwTogM7jVatWsWECRNijz32yHYpAAAA7KCqVasWOTk5UVRktqXdh8ywcxGiAzuNP/3pT/Hll1/Grbfemu1SAAAA2EFVq1Yt3nnnnWjWrFmxx7Vr166SKgKyTYgO7DQKCgri2GOPjU8++SS6dOmSsd/r2LFjs1QZAGTf+PHjY8SIEfHRRx9FTk5O7L333vHrX/86Dj300GyXBgCVqlq1arF48eJo3rx5tksBdhDJmx8CfM9cfPHF8frrr8eee+4ZTZo0iby8vLQbAOysHnzwwfjhD38YdevWjUsuuSQuuuiiqFOnTgwYMCAefvjhbJcHAABZZSU6sNNo0KBBPProo3HsscdmuxQA2KHsvffecf7558fll1+e1n7LLbfEvffeGx999FGWKgOAynf44YfHk08+GY0aNcp2KcAOQogO7DTatWsXL774Yuy1117ZLgUAdii5ubkxc+bM2H333dPa58yZE926dYtvv/02S5UBQPZt2LAhlixZEgUFBWntbdu2zVJFQGWznQuw0xg2bFjccMMNsW7dumyXAgA7lDZt2sSrr76a0f7qq69GmzZtslARAGTf7Nmz49BDD406depEu3btokOHDtGhQ4do3759dOjQIdvlAZWoRrYLAKgst912W3z66afRokWLaN++fcaFRd99990sVQYA2XXFFVfEJZdcEtOnT4++fftGTk5OvPnmmzFmzJj461//mu3yACArzjrrrKhRo0b8v//3/6JVq1aRk5OT7ZKALBGiAzuNE088MdslAMAO6X/+53+iZcuW8ec//zkef/zxiPjvPumPPfZYnHDCCVmuDgCyY/r06TFt2jRbggL2RAcAAACArfXs2TP+8pe/xCGHHJLtUoAsE6IDAAARETFt2rT46KOPIicnJ7p06RIHHHBAtksCgKx57bXX4rrrroubb7459tlnn4wtQRs2bJilyoDKJkQHdhqbN2+Ov/zlL/H444/H/PnzY8OGDWmPr1ixIkuVAUB2LVmyJH7605/GuHHjolGjRpFKpSI/Pz8OP/zwePTRR6NZs2bZLhEAKl21atUiIjL2Qk+lUpGTkxObN2/ORllAFtgTHdhp3HjjjXHffffFkCFDYujQoXHttdfGvHnz4qmnnorrr78+2+UBQNZcfPHFsXr16pg5c2bsvffeERHx4YcfxplnnhmXXHJJPPLII1muEAAq3+uvv57tEoAdhJXowE6jU6dOcdttt8Wxxx4bDRo0iOnTpxe2TZ48OR5++OFslwgAWZGXlxevvPJK9OzZM639nXfeiYEDB8aqVauyUxgAAOwArEQHdhqLFy+OffbZJyIi6tevH/n5+RERcdxxx8XQoUOzWRoAZFVBQUHGPq8RETVr1oyCgoIsVAQAO45169YVuSXovvvum6WKgMpWLdsFAFSW3XbbLRYtWhQREbvvvnu89NJLERExZcqUyM3NzWZpAJBVP/jBD+LSSy+NL7/8srBt4cKFcfnll8eAAQOyWBkAZM/SpUvjuOOOiwYNGkTXrl3jgAMOSLsBOw8hOrDTOOmkk+LVV1+NiIhLL700hg4dGnvssUcMHjw4zjnnnCxXBwDZc8cdd8SaNWuiffv20alTp9h9992jQ4cOsWbNmrj99tuzXR4AZMVll10WK1eujMmTJ0edOnXihRdeiPvvvz/22GOPePrpp7NdHlCJ7IkO7LQmT54cEydOjN133z2OP/74bJcDAFn38ssvx8cffxypVCq6dOkSP/zhD7NdEgBkTatWreLf//539OrVKxo2bBhTp06NPffcM55++un405/+FG+++Wa2SwQqiRAdAAAAALbSsGHDeP/996N9+/bRvn37eOihh+Lggw+OuXPnRteuXWPdunXZLhGoJC4sCnyvleYrdlajA7Azue2220p87CWXXLIdKwGAHVPnzp1j1qxZ0b59+9h///3j7rvvjvbt28ddd90VrVq1ynZ5QCWyEh34XqtWrWSXfsjJyYnNmzdv52oAYMfRoUOHEh2Xk5MTn3322XauBgB2PA899FBs3LgxzjrrrHjvvffiyCOPjOXLl0etWrVizJgxceqpp2a7RKCSCNGBnd78+fPjhhtuiNGjR2e7FADIui3/PMjJyclyJQCwY1m3bl18/PHH0bZt22jatGm2ywEqUcmWaAJ8j61cuTIeeOCBbJcBAFk1cuTI6NatW9SuXTtq164d3bp1i/vuuy/bZQHADqNu3bpx4IEHCtBhJ2RPdAAA2MkNHTo0/vKXv8TFF18cffr0iYiISZMmxeWXXx7z5s2Lm266KcsVAkDlGDJkSImPveWWW7ZjJcCOxHYuwE5vxowZceCBB9oTHYCdVtOmTeP222+P0047La39kUceiYsvvjiWLVuWpcoAoHLtsssu0a1bt6hRo0bk5OREUmyWk5MTr732WiVXB2SLlegAALCT27x5c/To0SOjvXv37rFp06YsVAQA2ZGfnx9PPPFENG/ePDp27BhTpkyJJk2aZLssIMuE6MD33sknn1zs46tWraqcQgBgB/Xzn/88/v73v2d8Lf2ee+6Jn/3sZ1mqCgAq3y677BJz586N5s2bx7x586KgoCDbJQE7ACE68L2Xl5e3zccHDx5cSdUAwI7hu3u+5uTkxH333RcvvfRS9O7dOyIiJk+eHAsWLPAeCcBOZdCgQXHYYYdF69atIycnJ3r06BHVq1cv8tjPPvuskqsDssWe6AAAsBM6/PDDS3ScPV8B2Nm88MILMWfOnLjkkkvid7/7XTRo0KDI4y699NJKrgzIFiE6AAAAAGzl7LPPjttuuy0xRAd2HkJ0AAAAAABIUC3bBQAAAAAAwI5KiA4AAAAAAAmE6AAAAAAAkECIDgAAAAAACYToAAAAAACQQIgOAAAAAAAJhOgAAAAAAJBAiA4AAAAAAAmE6AAAAAAAkECIDgAAAAAACYToAAAAAACQQIgOAAAAAAAJhOgAAAAAAJBAiA4AAFk2b968yMnJSbuNGTNmp5kfAAB2ZEJ0AAAAAABIIEQHAMoklUpF+/btM1av1qhRIxYuXJjt8gAAAKBCCNEBgDJ55ZVX4vPPP89o37x5s20gAEpp3LhxGR9Kjhs3LttlAQAQQnQAoIxGjhyZ+NioUaMilUpVYjUAAACwfQjRAYBSW7FiRTz11FOJj3/22WdWUAIAAPC9IEQHAErtwQcfjPXr16e15eTkpN0vbqU6AAAAVBVCdACg1EaNGpV2f4899oiTTz45rW3s2LGRn59fmWUBAABAhROiAwClMnXq1JgxY0Za2+DBg+PMM89Ma/vmm2/i4YcfrszSAAAAoMIJ0QGAUtl6m5acnJw444wz4uijj47mzZsXeywAAABUNTWyXQAAUHV888038cgjj6S1HXbYYdGuXbuIiDj99NPj1ltvLXxs2rRpMWPGjNhvv/0qs8ztYvXq1fHOO+/EV199FUuXLo1vv/02mjZtGs2aNYsePXrErrvuut1rWLJkSUydOjXmzp0b+fn5kZOTE02bNo2TTjopmjZtus3+K1eujI8//jhmz54dK1eujLVr10bdunWjcePG0aJFi+jVq1c0btx4u9ReUFAQU6ZMiU8//TQWLVoUGzdujMaNG8dee+0VBx10UOTm5m6Xebf2xRdfxIwZM2Lp0qWxdOnSyMnJiWbNmkWrVq2id+/e0bBhw0qpo6w2bdoUU6dOjZkzZ8ayZcuiWrVq0apVq+jQoUP07t07qlevnu0S03zzzTfxzjvvxKJFi2LJkiWxdu3aaNKkSTRr1iz222+/6NSpU7ZL3O5mz54dM2bMiC+++CLWrl0btWrVilatWsUZZ5yR7dJKJZVKxWeffRbvv/9+LF68OFavXh2bNm2KOnXqRP369WO33XaL9u3bx5577hk1alTcPzPz8/NjypQp8dVXX8Xy5ctjzZo1Ua9evWjcuHHsueee0aVLlwp73X7yySfx4YcfxpIlS2L58uVRr169aN68ebRp0yZ69eoVNWvWrJB5irNu3bp45513Yvbs2bFixYrYtGlT5OXlxeGHHx5du3Yt8TgffPBBzJkzp/Bc6tatG82aNYv27dtHz549K+VcAOB7JQUAUEIPPPBAKiLSbqNGjSp8/L333st4/JJLLtnmuOvXr081adIkrd/+++9f7npvvvnmjHqeffbZEvf/9ttvU7fffnvq0EMPTdWoUSNjrO/eunTpkrr55ptTa9asKXWd/fr1SxurX79+hY8VFBSkHnrooVTv3r1TOTk5Rc79+uuvFznuN998kxo7dmzq/PPPT+25557F1h8RqZycnFTXrl1TI0aMSK1evbrU51GUZcuWpS699NJUy5YtE+etX79+6rzzzkvNnTu3sN/rr79e4vPclq+++ip19dVXp7p27Vrs+deoUSN18MEHpx5++OHU5s2bK+T8S2ru3LkZ9YwePbrw8cWLF6cuu+yyVKNGjRLrb968eeqiiy5KLVmypMLnL43NmzenHnjggdQRRxyRql27drE/8w4dOqR+85vfpJYuXVrqedq1a5c21plnnlmmem+44YaMukpi6z433HBD4WNr165NDR8+PNWxY8fEc0+au7S3sp53Sc2aNSt1ySWXpFq0aFGieurUqZM69NBDU//7v/+bmjVrVpnmXLVqVep///d/U7169UpVr1692PmqV6+e6t27d+qmm25KffHFF6Wea+HChalLL7001aFDh2LnadCgQerEE08s89+hbT3PJk6cmDrppJNSubm5Rc7/3edXkhkzZqTOPvvsVOvWrYs9l/r166dOPPHE1KRJk8p0LgCwMxKiAwAltnXYW7du3Yywdd999007pnHjxqlvv/12m2NffPHFGf/Qf++998pVb+fOndPGa9WqVWrTpk0l6vvggw+m2rRpU+pAq1mzZqlHH320VHUmheiLFy9OHXbYYducs6hQ59Zbb001bNiwzMHcLrvsknriiSdKdR5be/TRRzM+HCnuVq9evdSYMWNSqVTFhOjr169PDR06NFWvXr1Sn3+3bt1SM2bMKNf5l0ZxIfazzz6baty4cYlrb9KkSeqRRx6psPlL4/nnn0916dKl1D/vBg0apG699dZSzbWjhuiTJ09OtW3bdpvnnDR3aW/bK0QvKChIXXfddalatWqVubYWLVqUas5NmzalbrrppmI/LCruVr169dTbb79d4rmGDh2aqlu3bqnnOeaYY1Kff/55qc4t6Xm2YcOG1K9+9avED0m3fn4V5csvv0ydfvrp2xyjqNsJJ5yQWr58eanOBQB2RvZEBwBKZM6cOTFhwoS0thNPPDEaNGiQ1rb1BUZXrFgRTz311DbHP/vsszPaRo8eXfpC/z8TJ06MWbNmpbUNHjx4m9tdFBQUxOWXXx4///nPY8GCBaWed+nSpfHTn/40brjhhlL3/a7FixdH3759M37mJfXee+/F6tWryzz/ypUr45RTTok//OEPZep/zz33xGmnnRbLly8vcZ+vv/46zjrrrLjrrrvKNOd3rVixIgYOHBi///3v4+uvvy51/w8++CAOPvjgeOaZZ8pdS3k888wzccIJJ8SKFStK3Gf58uVx+umnxz333LMdK8s0YsSIOPbYY+PDDz8sdd81a9bEZZddFueee25s2rRpO1RXOSZMmBD9+/eP+fPnZ7uUcjvzzDPjpptuig0bNlTKfCtXroyjjjoqrrvuuli1alWZxti8eXOsW7dum8etW7cuTjrppPj9739fouO39txzz0WfPn3i/fffL0uZhTZv3hynnHJK/O1vf4tUKlWmMWbMmBG9evWKhx9+uExj/Pvf/47evXvHJ598Uqb5AWBnYU90AKBERo0alfEP9K0D84iIn/3sZ3HVVVfF5s2bC9tGjhwZp556arHjH3DAAbH//vvH9OnTC9sefvjhGDFiRJn2bh0zZkxGW1FB/dZ+9rOfxaOPPprR3rp16xgwYEAccMAB0bRp06hdu3asWLEi3nvvvXj++eczQrPf/e530axZs7joootKXXtBQUH85Cc/ic8++6ywrWPHjnHsscfGXnvtFU2bNo3ly5fH3Llz41//+leJxuzcuXPst99+sffee0erVq2iQYMGUatWrVizZk3Mnz8/3nvvvXjppZfim2++KeyTSqXit7/9beyzzz5x7LHHlrj+J598Mi644IKM50v16tXj0EMPjYEDB8auu+4a1atXjy+//DJef/31ePXVVwvDuosuuihuuummEs+3tVWrVsXBBx8cH3/8ccZj3bp1i379+kXXrl2jUaNGEfHfveYnTZoUzz33XKxZs6bw2LVr18aPf/zjeOutt6J79+5lrqes5s6dGxdffHFhqJyTkxN9+/aNY445JnbbbbfIycmJBQsWxPPPPx9vvfVW2s87lUrFBRdcEE2aNIlBgwZt91qvvvrq+OMf/5jR3rhx4zjiiCOie/fu0bx586hbt26sWrUqZs6cGS+88ELGB10jR46MRo0axYgRI7Z7zRVt8eLFcfLJJ8e3335b2NarV68YOHBgtGvXLho0aBCLFi2KDz/8MP75z39GRETLli0Lrxmxdu3a+PTTT9PG7NSpU9SvX7/Yedu2bVvBZxJx//33xz/+8Y+M9mbNmsWRRx4Z++yzT7Ru3Trq1KkT33zzTaxZsybmzp0bM2fOjEmTJsXKlStLNV9+fn707du3yNdszZo1o0+fPtG/f//YddddY5dddomvv/46li5dGtOnT4/Jkyen/a3cloKCgjjhhBPilVdeyXisQYMG8aMf/Sh69eoVLVu2jNWrV8fs2bPjySefjDlz5qQd++WXX8Zhhx0WU6dOjd13371U57vF9ddfH08//XTh/caNG8fRRx8dPXv2jObNm8c333wTX3zxRTz//PORk5OT0X/q1Klx+OGHx9q1a9Paq1WrFoceemj07ds3OnToEI0aNSoca/z48fHqq6+mvUfPnj07jjnmmJg2bVrk5eWV6VwA4Hsvi6vgAYAqYtOmTRl7rLZu3Tpx3+hjjjkm7dhq1aql5s2bt815/vrXv2Z81bwsW4qsW7cuYyuTvn37brPfLbfckjH/brvtlnr88ceL3QZm48aNqXvvvTdVv379tL61atVKTZ06dZvzbr2dy3f3AG7SpEnqgQceSBUUFBTZt6CgoMjtcs4666xUnz59Uvfcc09qwYIF26whlUql1qxZkxo2bFjG9g3NmjVLrVu3rkRjLF26NNW8efOMn2PPnj2L3R7ls88+S/3whz8sPL5OnToZY5R0O5cTTzwxo2/fvn1TkydPLrbfypUrU0OGDMnYEqF9+/YVtkd8kqK2U/nufuJ77bVXsfW//fbbqb333jtjjGbNmpVov/HybOcyduzYjL677LJL6u6770598803if0KCgpSY8eOLfL58u9//3ub8+5o27l893W77777piZOnJjYt6ifS0VeB6C8dt9994xz+9Of/lSirbk2bdqUevPNN1Pnn39+qlOnTts8vqCgIHX88cdnnHtOTk7q3HPPLdE+59OnT0/9+te/TuXl5W3zZzZ8+PAitzU577zzUqtWrUrsN2rUqCK3menZs2dqw4YN26yxqOfZludM9erVU0OHDk2tXbs2sf/Wz5kVK1ak2rdvnzHm2Wefvc2tZubMmZM68sgjM/qefPLJ2zwPANhZCdEBgG165plnMv6xfdVVVyUe/9hjj2UcP2zYsG3Os2zZsowA90c/+lGp633wwQcz5r/33nuL7fOf//wnVbNmzbQ+vXv3LjZU2dr06dMzwvujjjpqm/22DtG33Fq0aJGaOXNmief/rtLUvbWXX34540Kqd999d4n6XnDBBRnnceihhxYbDm2xcePG1Mknn1zkz6KkgeLdd9+d0e/CCy9M/BCiKKNGjcoY4w9/+EOJ+5dFUSH2llvXrl1Ty5Yt2+YYy5YtK/Liqeecc06Z5i9JiP7VV1+l8vLy0vrtscceJf7gJpVKpebPn5/abbfd0sbo0qXLNn9nO1qIvuV28MEHp/Lz80tdx44Son/wwQdl+vtdlK+//nqbx/z973/PmC83N7fU+/qnUqlUfn5+sft7z507N+PvfESkbrrpphKNP3Xq1CKvNTFixIht9k3a/75atWqpxx9/vMTnuMVpp52WEcg/+OCDJe5fUFCQOvvsszPq2daHjQCwsxKiAwDbVNTK3g8++CDx+G+//TZjxV67du0SV65/16BBg9L61ahRI7V48eJS1TtgwIC0MYq6AOrWTj/99LQ+rVq1Sq1YsaJU86ZSRQf427pAalKI/swzz5R6/opy5ZVXptXSs2fPbfbJz8/PuIhnXl5eatGiRSWed+3atYkXZdxWoLhx48aMYLUkH2IU5dxzz834QKMkq3DLKilEr1WrVmrWrFklHmfWrFkZH0Tl5uZuczV6WUP03/72txmvtU8//bTE9W7x5ptvZsz/1FNPFdtnRwzR8/LySrRyuig7Soj+9NNPZ9SxcOHC7TLXhg0bMn6PEZG65557tst8V1xxRcZcpV19/eijj2aM0bZt221etDopRL/iiitKfR4ff/xxqlq1amnjlOWDvg0bNmR8g+XEE08s9TgAsDNwYVEAoFhLliyJZ599Nq3twAMPjK5duyb2yc3NzdgD/fPPP49XX311m/Odc845afc3bdpU5N68SebPnx+vvfZaWtspp5yScQHU75o3b148/vjjaW033XRT7LLLLiWed4vTTz899thjj7S2klxYdWuHH354HHfccaXuV1EGDx6cdv+9997b5gX4HnrooYyLeF599dXRsmXLEs9br169uPnmm0te6Hc8+uij8fnnnxfez8nJidtvv71MY11//fVpexB/9dVXMWnSpDKNVR4XX3xx7LnnniU+fs8994yLL744rW39+vVFXiOgvNauXRt33nlnWtsVV1wRHTt2LPVYBx98cAwYMCCt7cknnyxXfdkwZMiQ2HXXXbNdRrl897oAWzRp0mS7zLX1azYi4sgjj4zzzjuvwuf69ttvY+TIkWlttWrVittuu61U45x66qnRv3//tLb58+fHv//971LX1KBBgxg2bFip+/3f//1fFBQUFN7v0KFDXHnllaUep2bNmvHb3/42re35559P29cfAPgvIToAUKz7778/Nm7cmNZW1AVFt7Z1CBsRGQFGUY488sho3bp1WltpAsD7778/44KW27qg6FNPPVV48caI/wa5P/3pT0s853fl5OTE0UcfndY2fvz4Uo/zi1/8okzzV5StPwjYtGlTTJ06tdg+W394Ub169RI9V7Y2aNCgwot+lsbWF1nt379/mS/416ZNm9hnn33S2sryeyyvsoSJ559/fkbb888/XxHlpHnllVdi1apVaW3led5uffHabPy8yyMnJyfjQ8CqqKjA/M0339wuc333oppbXHXVVdtlrilTpmQ8X48//vgyfehx4YUXZrS9/PLLpR7n1FNP3eaFY7eWSqVi7NixaW1nnXVWVK9evdTzR0Qcc8wxaffXr18fb7/9dpnGAoDvMyE6AFCsUaNGpd2vUaNGnHbaadvs17dv3yJXZK9YsaLYftWrV48zzjgjrW3mzJkxZcqUEtV7//33p93v2LFj9OvXr9g+W4d1+++/f9StW7dE8xWlQ4cOafffe++9Uo9x+OGHl3n+omzevDnefPPNuOOOO+KXv/xlHH/88XH44YdHjx49Yv/998+49e7dO2OM+fPnFzvH5MmT0+4fcMAB0apVq1LXWrt27YxVyduSSqXijTfeSGvr27dvqef+ror4PZbHXnvtFZ07dy51vz333DPjmyJTpkxJW7laEbZ+3ey6667Rrl27Mo+39c973rx5GaHnjmz33XeP3XbbLdtllFvPnj2jWrX0fyaed955MXPmzAqdJ5VKFfkc+sEPflCh82zx1ltvZbSdcsopZRrrhBNOiNzc3G2Ovy1l+Tv//vvvx8qVK9PayvO3rnHjxpGXl5fWVtl/6wCgKqiR7QIAgB3XW2+9FR9//HFa29FHHx3NmjUrUf/BgwfH0KFDC++vX78+HnrooYztJrZ2zjnnxB//+Me0tjFjxkTPnj2L7TdhwoT49NNP09rOOuustG05irJ1+DFz5szYf//9i+1TnK0/KMjPz4+NGzdGzZo1S9S/efPmGavxy2rx4sXxhz/8IR599NH46quvyjVWcYHmqlWr4osvvkhrO/DAA8s81wEHHBBPPPFEiY//6KOPMn7u999/f/y///f/ylzD1h8aLFu2rMxjlUX37t3L3PfAAw9MCz3XrFkTn3zySey1114VUVpEZL5uVq5cWa7Xzdq1azPali1bVqZvJWRDeZ7vO5LGjRvH8ccfn7YN1dy5c2O//faLE044IU477bQYOHBgNGzYsFzzfPrpp7F06dK0tvJ+8FWcd999N6OtR48eZRqrVq1ase+++6Z9uDtz5sxYv359RrhenLI8Z4oK6y+++OJSzbu1rbfqquy/dQBQFQjRAYBERW2/UprtOc4444y4/vrr07ZXGTly5DZD9D333DP69u0bEydOLGx75JFH4pZbbik2KBg9enTa/WrVqm2z3g0bNmQEOatWrarwFbArVqyIFi1alOjYkh63Lffcc0/8+te/jtWrV1fIePn5+YmPLV++PKOtffv2ZZ5r61XJ27J1gL+lraj2sirqHLensqxC36KosHzJkiUVGqJv/bNdt25dzJgxo8LGj/jvz7ysW/JUtubNm2e7hAozYsSImDBhQtoHU5s3b46xY8fG2LFjo3r16nHAAQdEnz59omfPnnHYYYeV+lsIRX2ot/UWShVp62C4Vq1a0alTpzKP16VLl7QQvaCgIFauXFmqa0CU5TlT1N+0rT/sLq/K/lsHAFWB7VwAgCKtXbs242Kbu+yyS6kudtmuXbuMC7DNmDEjpk2bts2+W+9jvnLlymIv3Pb1119n7Ik9YMCAaNu2bbHzVFZY8M0335T42PKu8IyI+NOf/hS//OUvKyxAj4iMvfG/a+vtBSIiY4uA0iht38r4PZbmd1gRKvrntz0+GNreKvtnXh4V8brdUXTq1CleeeWVxA+zNm/eHFOnTo3bb789Bg8eHO3bt48OHTrEFVdcUeSK76IU9fwpy8WcS2rrv1HleX1FFF1raV8TZXnOfB//1gFAVSBEBwCK9Oijj8bXX3+d1nbqqaeW+ivjRa0EL8kFRk899dSMfcm3Xmn+Xf/85z8ztoPY1gVFI4oOf7OtRo3yfVnwzTffjN/85jcZ7fXq1YvTTz897rzzzhg3blzMmTMnVq5cGevWrYuCgoJIpVJpt9JYv359RlutWrXKfA6lfZ7tiL/H8qpXr16F9l2zZk15ykmzbt26In/nO7Pyvm53NAcccEB88MEH8cc//jHatGmzzePnzZsXt9xyS3Tv3j1++MMfxvTp04s9vqgP+Ep7kc3S2Pr5X57XV1L/0r7GyvKc+T7+rQOAqkCIDgAUqaig+6677oqcnJxS3c4666yMcR555JFtrnRr0KBBxkXfXnrppVi4cGGRx28dsDdq1ChOOumkbZxlRJ06dTLaLrvssoxAuby38mxtUlqXX355RttZZ50VX3zxRTz00EPxP//zP9GvX7/o1KlTNGrUKOrUqZOxb/y3335bqjmLWtVZntC2tCvoi/o9PvXUUxX6O5w3b16Zz6cstv4Qq7x9GzRoUJ5y0tSuXTvj4pMnnnhihb9utv4mC5Wrbt26cdVVV8Xnn38e48ePj+uuuy4OO+ywIl9v3/Xqq6/GQQcdlHGh5+8qahV2UfviV5Stn//leX0l9a/I11iSon72K1eurNDX3ZgxY7b7eQBAVSNEBwAyfPjhhzF58uTtNv6qVati7Nix2zxu65XkBQUF8Y9//CPjuM8++yzeeOONtLbTTjstateuvc05mjZtmtE2d+7cbfbbUc2ZMyemTp2a1vajH/0oRo8eXaoLNJZ2y4CitjYoz7YDpe37ffs9RhS/B31Z+lbkBTqrVauWMV5V/XkXt00R/5WTkxOHHXZY/P73v4/x48fH6tWrY8qUKXHLLbfEMcccU2Swu2HDhvjFL34REyZMKHLMJk2aZLRtz1XWW/+NKs/rK6Lo7ZEaN25crjFLoqi/dZX9AR8A7IyE6ABAhpJst1IZc/Tr1y86duyY1lbUCrkxY8ZkbD9Skq1cIv67cnDrAOj9998vUd8d0csvv5zRdv3115d6nM8++6xUxzdv3jxjC5b//Oc/pZ53i9L+Doq6GGtV/j1GRHzyySdl7jtr1qyMtoq+8OXWP/NPPvmkUrd4qVmzZtr9sobhLqJYejVq1IgePXrE5ZdfHs8++2wsWbIk7rrrrmjdunXacZs3b45f//rXRY5R1AU4t+drtlmzZmn3N2zYEJ9++mmZx/vwww/T7lerVq1SQvTv4986AKgKhOgAQJqNGzdmrPauVatW7LfffuW6bR0ujBs3bptBbVHbwcyaNSsmTZpUeD+VSsUDDzyQdkzXrl2jZ8+eJT7nXr16pd2fO3dukSFkVbBgwYK0+7Vr144ePXqUepzv/oxLombNmrH//vuntb3zzjtRUFBQ6rkjotTfhNh3330zvnnwwgsvlGnuHUVJLsBb0r4NGjSIPffcs7wlpdn6dfPNN9/EuHHjKnSO4my9HUhZL6I7Z86ciihnp1a/fv345S9/Ge+++27G/unvvPNOxt+liIiOHTtmBMITJ07cbjUeeOCBGW1bf2unpDZs2JARXHfr1q1c14Eoqa1fdxERzz///HafFwB2dkJ0ACDN008/HUuXLk1rO+mkk2L69Onlut10001pY6ZSqWIvFLrFmWeembH38nf7vfbaa/H555+nPX7OOeeU6pyPOOKIjLYHH3ywVGPsKJYtW5Z2v6wrIx9//PFS9+nTp0/a/cWLF8frr79e6nE++eSTUodbtWvXjkMOOSStbdGiRfHqq6+Wev4dxUcffVSmD3M++eSTmDlzZlpbz549M15H5ZXt183W28mU9tsTEf8NQ7fn1lWlUdRFJjdv3pyFSsquRYsWccUVV2S0J30rpV+/fmn3v/zyy+32mu3bt29G27/+9a8yjfXMM89kXDeiqPG3h759+2Zc1PTZZ591wVEA2M6E6ABAmqK2Wfn5z39e7nFPPfXUjFV6Y8aM2eZK5bZt28aAAQPS2h577LHCC5Nuvb1LjRo1Sl3v8ccfn9F22223xYoVK0o1zo5g63Bl5cqVpV4NPn78+DKtgj711FMz2v7v//6v1OOUpU9ExAknnJDRNmzYsDKNtaO47777St3n3nvvzWg7+uijK6KcNEceeWTG6v9HHnmk0r7F0blz57T7n3zySan3uX7ooYfKfYHJilLURSm354U2t5e99toroy3p91LUxZ/L+vrfll69emV88PLMM8/El19+Weqx7rzzzoy2gQMHlrW0UqlVq1YcddRRaW1r1qyJP//5z5UyPwDsrIToAEChhQsXxksvvZTW1qxZs4x/sJdF48aNM4K8L774Il588cVt9t16f/PVq1fH2LFjC//3u4499thS7/28zz77ZATpq1evjjPOOKPM25FkS6tWrdLuf/PNNxkXXS3OunXr4vzzzy/T3L17944DDjggre3FF18s1erk1157rcx78v/iF7/I2Gf5zTffjD/+8Y9lGm9HcPvtt5dqu5E5c+bE7bffntaWm5ubsS1SRWjatGnGc2Xz5s1x+umnF37ItT1179497f6mTZtK9Q2K/Pz8+P3vf1/RZZVZURfnLcvq+mxbtGhRRtvW+5Fv8eMf/zjjuhcvvvhikR8ElVdubm6ce+65aW3r16+Pyy67rFTj/Otf/4rXXnstra1du3ZFfhi7vVx77bUZbX/605/izTffrLQaAGBnI0QHAAqNHj06Y/uAU089tchtBsqiqBXiJQlMTzrppIwVhKNHj47HHnss1q1bl9Ze2q1ctvjd734X1atXT2t77rnn4uyzz8742n5JzZw5MwYPHlypX7M/9NBDM9quvfbaEl108dtvv40f//jH5bqgZVEXMf3FL36R8WFHUSZMmBAnnnhixkViS6pOnTpFhku//e1v44477ijTmBH/3Vv9wgsvLHP/8li/fn2cfPLJJXoOrVy5Mk4++eSMi3uefvrp0bRp0+1S3zXXXJPx7Yd33303TjrppDI/7z///PO4+OKL44MPPij2uIEDB2ZsUXPjjTeWaDX6t99+Gz//+c9j7ty5Zapxe2jTpk3k5eWltT333HOVXseYMWPiiSeeKNNWMhs3bsxYpV2tWrXo1q1bkcdXr169yNfsxRdfHI899lip51+9enWx3yC66KKLMi5I+89//rPEq9/fe++9jCA+IuLSSy/NeP/Yng444IAYNGhQWtvGjRvjpJNOigkTJpRpzPXr18c999wTf/nLXyqiRAD4/kkBAKRSqYKCglTHjh1TEZF2mzx5coXN8e2336by8vLSxq9Zs2ZqyZIl2+z7P//zP2n9qlWrlurcuXNaW/PmzVMbN24sc31//vOfM84/IlL77bdf6umnn04VFBRsc4wVK1akRo4cmTriiCNSOTk5qYhILV26tNg+/fr1S5uvX79+ZT6HTZs2pdq1a5dxDscdd1yxdbzzzjupHj16FB7fsGHDjDFuuOGGEtXwk5/8pMif409+8pPUhAkTUps3b047fsqUKanzzz8/Va1atcJj+/Tpk9H/9ddf3+bcBQUFqRNOOKHI+U888cTU9OnTS3QOn332WeoPf/hDap999klFRKpr164l6ldWc+fOzai3du3ahf/dpUuX1Ntvv53Y/5133kl17do1Y4xmzZqV6PVV1PyjR48uUe3/+te/Cp/r37116NAh9Y9//KNEr8m1a9emHn300dRJJ52UqlGjRioiUlOmTNlmv6OPPjpj3oMOOig1f/78xD7Tpk1LHXTQQUX+nLfcSqKsr4/iHHXUURnjXn311amvvvqq3GOX1KWXXpqKiFT79u1T1157bWratGkl6jdv3rwi6x84cOA2+w4aNCijX05OTur8889PffHFF9vs/8EHH6R+85vfpPLy8rb5d+KPf/xjkX8fLrzwwlR+fn5iv/vvvz+1yy67ZPTr1atXiZ7jN9xwQ5meZ0mWLVuW6tChQ8aYNWrUSF155ZWpRYsWlWicyZMnp4YMGZJq2bJlKiJSv/rVr8pVFwB8X+WkUmVc6gMAfK+89tprGXuP77HHHuValVyU8847L2Of51tuuSUuv/zyYvtNmTIlevXqVewxV1xxRYwYMaJc9Z177rmJq+Pbtm0bhx9+eOy///7RpEmTqF27duTn58fKlSvj448/jmnTpsXMmTNj06ZNaf2WLl1a7Erg/v37x/jx4wvv9+vXL8aNG1fmcxg9enSRK/Lr1asXgwYNit69e0fTpk1j7dq1MX/+/Hj++efjnXfeSVsB/sADD8TgwYPT+t9www0l2mN8xYoV0b9//8SLCdatWzdatmwZ1atXj0WLFmXs+3zggQfG//7v/2Zs/zNhwoQiV9pvbe3atXHIIYfEjBkzinx8v/32i/79+8cee+wRTZo0iYiIVatWxbJly+L999+PadOmZWyj0bVr122ujC6PefPmRYcOHdLarr/++rjlllsKfz45OTlxyCGHxNFHHx1t2rSJiIgFCxbECy+8EG+88UbGCv6cnJz45z//mbFitaTzjx49usTbwNx0000xdOjQIh9r0aJF9O/fP3r06BHNmjWLevXqxerVq2PVqlUxZ86cmDp1arz//vsZK+inTJkSPXr0KHbeadOmRe/evTNec3Xq1ImTTz45Dj744MLn+ueffx6vvvpqvPXWW4U/qz322CN+9KMfxS233JLWvyT/RMrJyUm7X9LXR3H++c9/xk9+8pMiH2vVqlU0btw445tBxx9/fPzud78r17zfddlll8Vf//rXjLkPPPDA2H///aN169bRqFGjqFWrVqxduzbmzZsXEydOjHHjxmV84yU3NzemTp2auBJ9i9WrV0efPn3iww8/zHisZs2a0bdv3zj88MOjdevWscsuu8TXX39d+HqdPHly2vvU66+/Hv3790+cq6CgII488sh45ZVXMh7Ly8uL448/Pnr27BktWrSI1atXx5w5c2Ls2LExe/bsIo+fOnVq7L777sWeX8R/r89w4403prWV95/iH3zwQRxyyCFFfvuiVq1a0adPnzjssMNit912i1122SXWr18fq1atikWLFsV7770XU6dOzbiQ+K9+9atyfXMHAL63shjgAwA7kNNPPz1jRduNN95Y4fOMGzcuY56SrvLt1q1bkSsIt9w++OCDctdXUFCQuummm9JWRZf3Vpkr0bc47bTTylzvtddem0qlyrfSdunSpan999+/1HN369Yt9eWXX6ZefvnljMdKuoo8lUqlVq9eXeTq1rLesrESffTo0al///vfqerVq5e63pycnNTdd99d7vlLY+TIkUWu6i7rrSQr0VOpVOrGG28s0/itWrVKzZkzp8wrhMvz+kiyefPm1IABA0p1HmeeeWa55/2uLSvRy3vLzc1NPf744yWed8WKFaU+96JuJfnGytdff5067rjjyjVP69atS/U3qaJXom/xySefbPO9sTQ3K9EBoGj2RAcAYtWqVUXuWf2zn/2swuc67LDDom3btmltM2fOjLfffnubfbe+wOh39erVK7p27Vru+nJycuLaa6+NV155ZZurYLelYcOGce6550b9+vXLXVdpjR49usi9e4tTu3btuO222+Kmm24q9/xNmzaNyZMnxw033BC5ubnbPL569epx/vnnx1tvvRWtWrUqcj/trfeLLk6DBg3iX//6V/z973+PXXfdtVS1b61t27bFPve2p+OPPz6eeuqpjGsCFKdx48bx0EMPlfkCsWV1zjnnxKRJk+IHP/hBucapXbt2/PSnP834O5Hk+uuvj9/97ncZK8OLc+CBB8akSZOiU6dOZS1zu6hWrVr861//itNPPz1rNTRv3rxUP8uidOvWLV577bX48Y9/XOI+u+yyS7zwwgsxbNiwaNCgQZnmrV27djRs2HCbx9WtWzeeeuqpuO6666JOnTqlnueoo46KSZMmxX777VeWMivUHnvsEW+//XYMGTIk4/oEpdWjR4845phjKqgyAPh+EaIDAPHQQw9lXDyzT58+2yVgysnJKTKcL8kFRn/+859nXBRui4oOOQ8//PCYMmVKvPDCC/HjH/84mjVrVqJ+HTp0iHPPPTf++c9/xuLFi+Pee++N2rVrV2htJZGbmxv33ntvPPvss3HwwQcXe2xeXl788pe/jJkzZ8bFF19coTUMGzYs5s2bF3fccUcMHDgwdt9996hXr17UqlUrWrZsGf37948bb7wxZs+eHXfffXdhALZ48eKM8Ro3blzqGi644IL47LPP4t57740f/vCHUbdu3W32qVatWhxwwAHx61//Ol5//fWYN29eXHHFFaWeu6Icd9xx8eGHH8avfvWrYgPCZs2axUUXXRQff/xxnHbaaZVY4f/f/vvvH6+++mpMnjw5Bg8eHLvttluJ+rVq1Sp+/vOfx/333x+LFy+ORx55JJo3b17ieYcOHRrvvPNOHHnkkcVeCHn33XeP22+/Pd5+++1o165dicevTI0aNYqHHnooPv744xg2bFgcd9xx0alTp9hll10S//5VpN/+9rexePHiGDVqVPzsZz/L2OYnSc2aNePoo4+Ohx56KKZPnx59+/Yt9dw1atSIG264IebOnRtDhw6Nfffdd5uBfq1ataJ///7xl7/8JRYuXBgHHnhgieaqXr16/P73v4/Zs2fHJZdcEu3bty/2+Pr168cJJ5wQr732Wjz//PMl/pCnMtStWzf+/Oc/x7x582LYsGHRo0ePEl3otHbt2vGDH/wgbr755pg5c2ZMmTJFiA4ACeyJDgBQAqlUKj788MP45JNPYvny5bF8+fIoKCiIBg0aRKNGjaJTp06x1157xS677JLtUov01VdfxVtvvRVffvllrFq1KnJzc6NFixax9957xwEHHFBs8JgNP/vZz+Lhhx8uvN+mTZuYP39+ucfdsGFDTJs2Lb744otYtmxZrFy5MmrUqBENGjSIpk2bxp577hl77rlnmVanVoaNGzfGlClTYubMmbF8+fKoVq1atGrVKjp06BB9+vQpUXBW2ebMmRMffvhh4etmw4YNUb9+/cjLy4sOHTrEXnvtVarAfFvy8/PjjTfeiIULF8by5cujRo0aseuuu8aBBx4Ye++9d4XNszNZvnx5zJ49Oz777LNYtmxZ4T79DRo0iMaNG8fee+8dXbt2LdG3Tkpr8eLF8e6778aSJUti2bJlhc+fpk2bRufOnWPvvfcu0YdjJTFr1qyYOXNmLF26NJYvXx716tWLZs2aRdu2baNXr15Rq1atCpmnMuTn58eUKVNiyZIlsXz58sjPz486depEgwYNonXr1tG5c+fo2LHjDvk3AwB2REJ0AAB2KOvXr4+2bdvGkiVLCtsGDRoU//rXv7JYFQAAsLOynQsAADuU+++/Py1Aj/jvXvoAAADZYCU6AAA7jE8//TS6d+8e+fn5hW116tSJL774okx7ogMAAJSXlegAAFS46667rtR7mE+aNCn69euXFqBH/Hd/dAE6AACQLVUuRL/zzjujQ4cOUbt27ejevXu88cYbxR4/fvz46N69e9SuXTs6duwYd911VyVVCgCw8xoxYkR07NgxBg4cGH/729/i/fffj40bN2Yct2LFinjmmWfipJNOikMOOSQWLlyY9nirVq1i+PDhlVU2AABAhiq1nctjjz0WZ5xxRtx5551x8MEHx9133x333XdffPjhh9G2bduM4+fOnRvdunWL8847L375y1/GW2+9FRdeeGE88sgjMWjQoCycAQDAzqF27dqxfv36tLaaNWtG8+bNIy8vLzZt2hQrV66MZcuWRdL/Ha1Vq1Y899xzMWDAgMooGQAAoEhVKkQ/6KCD4sADD4y///3vhW177713nHjiiUWuUPrNb34TTz/9dHz00UeFbRdccEHMmDEjJk2aVCk1AwDsjIoK0UujefPm8eSTT0bfvn0rsCoAAIDSq5HtAkpqw4YNMW3atLj66qvT2gcOHBgTJ04sss+kSZNi4MCBaW1HHnlkjBw5MjZu3Bg1a9bM6LN+/fq0f/AVFBTEihUrokmTJpGTk1MBZwIA8P133HHHxQsvvBBff/11qfrVr18/zjrrrLj00kujefPmsXr16u1UIQAAsLNLpVKxZs2aaN26dVSrlrzzeZUJ0ZctWxabN2+OFi1apLW3aNEiFi9eXGSfxYsXF3n8pk2bYtmyZdGqVauMPsOHD48bb7yx4goHAKDE1q5dG3fccUfccccd2S4FAADYSSxYsCB22223xMerTIi+xdarwVOpVLErxIs6vqj2La655poYMmRI4f38/Pxo27ZtLFiwIBo2bFjWsqu0bje8mO0SACihD248Mtsl7FS8RwJUHd4jK5f3SICqY2d+j1y9enW0adMmGjRoUOxxVSZEb9q0aVSvXj1j1fmSJUsyVptv0bJlyyKPr1GjRjRp0qTIPrm5uZGbm5vR3rBhw502RK+WWzfbJQBQQjvre1W2eI8EqDq8R1Yu75EAVYf3yOQF11skb/Syg6lVq1Z07949Xn755bT2l19+OfGCU3369Mk4/qWXXooePXoUuR86AAAAAAB8V5UJ0SMihgwZEvfdd1+MGjUqPvroo7j88stj/vz5ccEFF0TEf7diGTx4cOHxF1xwQXz++ecxZMiQ+Oijj2LUqFExcuTIuPLKK7N1CgAAAAAAVCFVZjuXiIhTTz01li9fHr/73e9i0aJF0a1bt3juueeiXbt2ERGxaNGimD9/fuHxHTp0iOeeey4uv/zy+Nvf/hatW7eO2267LQYNGpStUwAAAAAAoAqpUiF6RMSFF14YF154YZGPjRkzJqOtX79+8e67727nqgAAAAAA+D6qUtu5AAAAAABAZRKiAwAAAABAAiE6AAAAAAAkEKIDAAAAAEACIToAAAAAACQQogMAAAAAQAIhOgAAAAAAJBCiAwAAAABAAiE6AAAAAAAkEKIDAAAAAEACIToAAAAAACQQogMAAAAAQAIhOgAAAAAAJBCiAwAAAABAAiE6AAAAAAAkEKIDAAAAAEACIToAAAAAACQQogMAAAAAQAIhOgAAAAAAJBCiAwAAAABAAiE6AAAAAAAkEKIDAAAAAEACIToAAAAAACQQogMAAAAAQAIhOgAAAAAAJBCiAwAAAABAAiE6AAAAAAAkEKIDAAAAAEACIToAAAAAACQQogMAAAAAQAIhOgAAAAAAJBCiAwAAAABAAiE6AAAAAAAkEKIDAAAAAEACIToAAAAAACQQogMAAAAAQAIhOgAAAAAAJBCiAwAAAABAAiE6AAAAAAAkEKIDAAAAAEACIToAAAAAACQQogMAAAAAQAIhOgAAAAAAJBCiAwAAAABAAiE6AAAAAAAkEKIDAAAAAEACIToAAAAAACQQogMAAAAAQAIhOgAAAAAAJBCiAwAAAABAAiE6AAAAAAAkEKIDAAAAAEACIToAAAAAACQQogMAAAAAQAIhOgAAAAAAJBCiAwAAAABAAiE6AAAAAAAkEKIDAAAAAEACIToAAAAAACQQogMAAAAAQAIhOgAAAAAAJBCiAwAAAABAAiE6AAAAAAAkEKIDAAAAAEACIToAAAAAACQQogMAAAAAQAIhOgAAAAAAJBCiAwAAAABAAiE6AAAAAAAkEKIDAAAAAEACIToAAAAAACQQogMAAAAAQAIhOgAAAAAAJBCiAwAAAABAAiE6AAAAAAAkEKIDAAAAAEACIToAAAAAACQQogMAAAAAQAIhOgAAAAAAJBCiAwAAAABAAiE6AAAAAAAkEKIDAAAAAEACIToAAAAAACQQogMAAAAAQAIhOgAAAAAAJBCiAwAAAABAAiE6AAAAAAAkEKIDAAAAAEACIToAAAAAACQQogMAAAAAQAIhOgAAAAAAJBCiAwAAAABAAiE6AAAAAAAkEKIDAAAAAEACIToAAAAAACQQogMAAAAAQAIhOgAAAAAAJBCiAwAAAABAAiE6AAAAAAAkEKIDAAAAAEACIToAAAAAACQQogMAAAAAQAIhOgAAAAAAJBCiAwAAAABAAiE6AAAAAAAkEKIDAAAAAEACIToAAAAAACQQogMAAAAAQAIhOgAAAAAAJBCiAwAAAABAAiE6AAAAAAAkEKIDAAAAAEACIToAAAAAACQQogMAAAAAQAIhOgAAAAAAJBCiAwAAAABAAiE6AAAAAAAkEKIDAAAAAEACIToAAAAAACQQogMAAAAAQAIhOgAAAAAAJBCiAwAAAABAAiE6AAAAAAAkEKIDAAAAAEACIToAAAAAACQQogMAAAAAQAIhOgAAAAAAJBCiAwAAAABAAiE6AAAAAAAkqDIh+sqVK+OMM86IvLy8yMvLizPOOCNWrVqVePzGjRvjN7/5Teyzzz5Rr169aN26dQwePDi+/PLLyisaAAAAAIAqrcqE6KeffnpMnz49XnjhhXjhhRdi+vTpccYZZyQev27dunj33Xdj6NCh8e6778bYsWPjk08+ieOPP74SqwYAAAAAoCqrke0CSuKjjz6KF154ISZPnhwHHXRQRETce++90adPn5g1a1Z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",
      "text/plain": [
       "<Figure size 1500x6400 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# Define the list of scores\n",
    "scores = ['blue score', 'meteor score', 'rouge score', 'SentenceSim score', 'SimHash score', 'perplexity score', 'bleurt score', 'F1 score']\n",
    "\n",
    "# Calculate the number of rows and columns for subplots\n",
    "num_plots = len(scores)\n",
    "num_cols = 1\n",
    "num_rows = (num_plots + num_cols - 1) // num_cols\n",
    "\n",
    "# Create the subplots with appropriate size\n",
    "fig, axes = plt.subplots(num_rows, num_cols, figsize=(15, 8*num_rows))\n",
    "\n",
    "# Flatten the axes array to iterate through\n",
    "axes = axes.flatten()\n",
    "\n",
    "# Load all CSV files into a list of dataframes\n",
    "csv_files = glob.glob(\"*.csv\")\n",
    "dfs = [pd.read_csv(file) for file in csv_files]\n",
    "\n",
    "\n",
    "# Iterate over the scores and create subplots\n",
    "for i, score in enumerate(scores):\n",
    "    averages = []\n",
    "    for df, file in zip(dfs, csv_files):\n",
    "        model_name = file.split(\".\")[0]\n",
    "        avg_score = df[score].mean()\n",
    "        averages.append({\"model_name\": model_name, \"average_score\": avg_score})\n",
    "\n",
    "    # Create a new DataFrame to store the average score for each model\n",
    "    results_df = pd.DataFrame(averages)\n",
    "\n",
    "    # Sort the DataFrame by the average score in descending order\n",
    "    results_df = results_df.sort_values(by=\"average_score\", ascending=False)\n",
    "\n",
    "    # Create a bar chart for the current score\n",
    "    ax = axes[i]\n",
    "    ax.bar(results_df[\"model_name\"], results_df[\"average_score\"])\n",
    "    ax.set_title(f\"Average {score}\", fontsize=36) \n",
    "    ax.set_ylabel(\"Score\")\n",
    "    ax.tick_params(axis='x', rotation=90)\n",
    "\n",
    "    # Modify x-axis labels\n",
    "    modified_labels = [label.split(\"_\")[-1] for label in results_df[\"model_name\"]]\n",
    "    ax.set_xticks(range(len(modified_labels)))  # Set the tick positions\n",
    "    ax.set_xticklabels(modified_labels)  # Set the tick labels\n",
    "\n",
    "    # Rotate x-axis labels for the second last bar chart\n",
    "    if i == num_plots - 2:\n",
    "        ax.tick_params(axis='x', rotation=0)\n",
    "\n",
    "# Remove any unused subplots\n",
    "if len(scores) < num_plots:\n",
    "    for j in range(len(scores), num_plots):\n",
    "        fig.delaxes(axes[j])\n",
    "\n",
    "# Adjust spacing between subplots\n",
    "fig.tight_layout()\n",
    "\n",
    "# Display the plots\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "30dbd765",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>question</th>\n",
       "      <th>context</th>\n",
       "      <th>ideal_answer</th>\n",
       "      <th>answer</th>\n",
       "      <th>blue score</th>\n",
       "      <th>meteor score</th>\n",
       "      <th>rouge score</th>\n",
       "      <th>SentenceSim score</th>\n",
       "      <th>SimHash score</th>\n",
       "      <th>perplexity score</th>\n",
       "      <th>bleurt score</th>\n",
       "      <th>F1 score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Identify the odd one out.</td>\n",
       "      <td>Twitter, Instagram, Telegram</td>\n",
       "      <td>Telegram</td>\n",
       "      <td>1. Twitter is a microblogging platform. 2. In...</td>\n",
       "      <td>1.115425e-312</td>\n",
       "      <td>0.002953</td>\n",
       "      <td>0.013889</td>\n",
       "      <td>0.472057</td>\n",
       "      <td>27</td>\n",
       "      <td>1.933278</td>\n",
       "      <td>-0.886688</td>\n",
       "      <td>0.016393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Explain why the following fraction is equivale...</td>\n",
       "      <td>4/16</td>\n",
       "      <td>The fraction 4/16 is equivalent to 1/4 because...</td>\n",
       "      <td>. a. 3/8 b. 2/4 c. 1/2 d. 1/4 a. 3/8 = 3/8 + 0...</td>\n",
       "      <td>8.044949e-234</td>\n",
       "      <td>0.009036</td>\n",
       "      <td>0.056338</td>\n",
       "      <td>0.248900</td>\n",
       "      <td>35</td>\n",
       "      <td>29.994287</td>\n",
       "      <td>-0.884714</td>\n",
       "      <td>0.017094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Evaluate this sentence for spelling and gramma...</td>\n",
       "      <td>He finnished his meal and left the resturant</td>\n",
       "      <td>He finished his meal and left the restaurant.</td>\n",
       "      <td>. He finnished his meal and left the resturant...</td>\n",
       "      <td>1.598205e-01</td>\n",
       "      <td>0.373583</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.749773</td>\n",
       "      <td>13</td>\n",
       "      <td>8.281993</td>\n",
       "      <td>-0.712102</td>\n",
       "      <td>0.476190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Classify the following into animals, plants, a...</td>\n",
       "      <td>Oak tree, copper ore, elephant</td>\n",
       "      <td>Oak tree: Plant\\n Copper ore: Mineral\\n Elepha...</td>\n",
       "      <td>. Oak tree, copper ore, elephant are animals, ...</td>\n",
       "      <td>3.513310e-155</td>\n",
       "      <td>0.384909</td>\n",
       "      <td>0.842105</td>\n",
       "      <td>0.938463</td>\n",
       "      <td>25</td>\n",
       "      <td>8.912095</td>\n",
       "      <td>-0.054478</td>\n",
       "      <td>0.526316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Reverse engineer this code to create a new ver...</td>\n",
       "      <td>def factorialize(num):\\n  factorial = 1\\n  for...</td>\n",
       "      <td>def factorialize(num):\\n  factorial = 1\\n  for...</td>\n",
       "      <td>of factorialize that returns the factorial of...</td>\n",
       "      <td>1.881367e-02</td>\n",
       "      <td>0.146354</td>\n",
       "      <td>0.340426</td>\n",
       "      <td>0.691330</td>\n",
       "      <td>13</td>\n",
       "      <td>15.535948</td>\n",
       "      <td>-0.914533</td>\n",
       "      <td>0.325000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>Discuss the impact of disruptive technology on...</td>\n",
       "      <td>Media industry</td>\n",
       "      <td>The media industry has seen a huge shift in ho...</td>\n",
       "      <td>Discuss the impact of disruptive technology o...</td>\n",
       "      <td>5.233751e-79</td>\n",
       "      <td>0.086374</td>\n",
       "      <td>0.168067</td>\n",
       "      <td>0.352136</td>\n",
       "      <td>26</td>\n",
       "      <td>51.848925</td>\n",
       "      <td>-0.332087</td>\n",
       "      <td>0.120603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>Evaluate the usability of a given app.</td>\n",
       "      <td>Evernote</td>\n",
       "      <td>Evernote is a great app for anyone looking to ...</td>\n",
       "      <td>Why or why not? Evernote is a note-taking app...</td>\n",
       "      <td>3.196735e-02</td>\n",
       "      <td>0.143134</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.793916</td>\n",
       "      <td>16</td>\n",
       "      <td>44.720349</td>\n",
       "      <td>-0.385405</td>\n",
       "      <td>0.232558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>Generate a headline for a news article about a...</td>\n",
       "      <td>noinput</td>\n",
       "      <td>\"Cyberattack Hits Businesses on a Global Scale\".</td>\n",
       "      <td>The headline should be catchy and attention-g...</td>\n",
       "      <td>2.309710e-236</td>\n",
       "      <td>0.009407</td>\n",
       "      <td>0.025316</td>\n",
       "      <td>0.330474</td>\n",
       "      <td>22</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>-1.051925</td>\n",
       "      <td>0.027397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>Make this sentence more polite.</td>\n",
       "      <td>You should do that as soon as possible</td>\n",
       "      <td>If possible, could you please do that as soon ...</td>\n",
       "      <td>You should do that as soon as possible. You s...</td>\n",
       "      <td>2.238844e-07</td>\n",
       "      <td>0.042962</td>\n",
       "      <td>0.084656</td>\n",
       "      <td>0.382615</td>\n",
       "      <td>17</td>\n",
       "      <td>9.581133</td>\n",
       "      <td>-0.976394</td>\n",
       "      <td>0.084656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>Generate a security tip for protecting user data.</td>\n",
       "      <td>noinput</td>\n",
       "      <td>Always use two-factor authentication to protec...</td>\n",
       "      <td>Answer:\\nThe answer is a security tip for pro...</td>\n",
       "      <td>1.458580e-158</td>\n",
       "      <td>0.013789</td>\n",
       "      <td>0.039604</td>\n",
       "      <td>0.398469</td>\n",
       "      <td>35</td>\n",
       "      <td>20.892721</td>\n",
       "      <td>-0.966411</td>\n",
       "      <td>0.036364</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              question  \\\n",
       "0                            Identify the odd one out.   \n",
       "1    Explain why the following fraction is equivale...   \n",
       "2    Evaluate this sentence for spelling and gramma...   \n",
       "3    Classify the following into animals, plants, a...   \n",
       "4    Reverse engineer this code to create a new ver...   \n",
       "..                                                 ...   \n",
       "995  Discuss the impact of disruptive technology on...   \n",
       "996             Evaluate the usability of a given app.   \n",
       "997  Generate a headline for a news article about a...   \n",
       "998                    Make this sentence more polite.   \n",
       "999  Generate a security tip for protecting user data.   \n",
       "\n",
       "                                               context  \\\n",
       "0                         Twitter, Instagram, Telegram   \n",
       "1                                                 4/16   \n",
       "2         He finnished his meal and left the resturant   \n",
       "3                       Oak tree, copper ore, elephant   \n",
       "4    def factorialize(num):\\n  factorial = 1\\n  for...   \n",
       "..                                                 ...   \n",
       "995                                     Media industry   \n",
       "996                                           Evernote   \n",
       "997                                            noinput   \n",
       "998             You should do that as soon as possible   \n",
       "999                                            noinput   \n",
       "\n",
       "                                          ideal_answer  \\\n",
       "0                                             Telegram   \n",
       "1    The fraction 4/16 is equivalent to 1/4 because...   \n",
       "2        He finished his meal and left the restaurant.   \n",
       "3    Oak tree: Plant\\n Copper ore: Mineral\\n Elepha...   \n",
       "4    def factorialize(num):\\n  factorial = 1\\n  for...   \n",
       "..                                                 ...   \n",
       "995  The media industry has seen a huge shift in ho...   \n",
       "996  Evernote is a great app for anyone looking to ...   \n",
       "997   \"Cyberattack Hits Businesses on a Global Scale\".   \n",
       "998  If possible, could you please do that as soon ...   \n",
       "999  Always use two-factor authentication to protec...   \n",
       "\n",
       "                                                answer     blue score  \\\n",
       "0     1. Twitter is a microblogging platform. 2. In...  1.115425e-312   \n",
       "1    . a. 3/8 b. 2/4 c. 1/2 d. 1/4 a. 3/8 = 3/8 + 0...  8.044949e-234   \n",
       "2    . He finnished his meal and left the resturant...   1.598205e-01   \n",
       "3    . Oak tree, copper ore, elephant are animals, ...  3.513310e-155   \n",
       "4     of factorialize that returns the factorial of...   1.881367e-02   \n",
       "..                                                 ...            ...   \n",
       "995   Discuss the impact of disruptive technology o...   5.233751e-79   \n",
       "996   Why or why not? Evernote is a note-taking app...   3.196735e-02   \n",
       "997   The headline should be catchy and attention-g...  2.309710e-236   \n",
       "998   You should do that as soon as possible. You s...   2.238844e-07   \n",
       "999   Answer:\\nThe answer is a security tip for pro...  1.458580e-158   \n",
       "\n",
       "     meteor score  rouge score  SentenceSim score  SimHash score  \\\n",
       "0        0.002953     0.013889           0.472057             27   \n",
       "1        0.009036     0.056338           0.248900             35   \n",
       "2        0.373583     0.500000           0.749773             13   \n",
       "3        0.384909     0.842105           0.938463             25   \n",
       "4        0.146354     0.340426           0.691330             13   \n",
       "..            ...          ...                ...            ...   \n",
       "995      0.086374     0.168067           0.352136             26   \n",
       "996      0.143134     0.250000           0.793916             16   \n",
       "997      0.009407     0.025316           0.330474             22   \n",
       "998      0.042962     0.084656           0.382615             17   \n",
       "999      0.013789     0.039604           0.398469             35   \n",
       "\n",
       "     perplexity score  bleurt score  F1 score  \n",
       "0            1.933278     -0.886688  0.016393  \n",
       "1           29.994287     -0.884714  0.017094  \n",
       "2            8.281993     -0.712102  0.476190  \n",
       "3            8.912095     -0.054478  0.526316  \n",
       "4           15.535948     -0.914533  0.325000  \n",
       "..                ...           ...       ...  \n",
       "995         51.848925     -0.332087  0.120603  \n",
       "996         44.720349     -0.385405  0.232558  \n",
       "997          8.000000     -1.051925  0.027397  \n",
       "998          9.581133     -0.976394  0.084656  \n",
       "999         20.892721     -0.966411  0.036364  \n",
       "\n",
       "[1000 rows x 12 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "6e21dfd8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SuperKnowa_Coqa_flanT5 \n",
      " blue score            0.093683\n",
      "meteor score          0.318327\n",
      "rouge score           0.382027\n",
      "SentenceSim score     0.616914\n",
      "SimHash score        23.842000\n",
      "perplexity score     20.313163\n",
      "bleurt score         -0.400201\n",
      "F1 score              0.340996\n",
      "Name: mean, dtype: float64 \n",
      "\n",
      "\n",
      "SuperKnowa_Coqa_Llama \n",
      " blue score            0.016324\n",
      "meteor score          0.119041\n",
      "rouge score           0.203716\n",
      "SentenceSim score     0.484394\n",
      "SimHash score        26.096000\n",
      "perplexity score     21.272829\n",
      "bleurt score         -0.929731\n",
      "F1 score              0.175145\n",
      "Name: mean, dtype: float64 \n",
      "\n",
      "\n",
      "SuperKnowa_Coqa_bloom \n",
      " blue score            0.018687\n",
      "meteor score          0.088058\n",
      "rouge score           0.141556\n",
      "SentenceSim score     0.463152\n",
      "SimHash score        26.199000\n",
      "perplexity score     21.126773\n",
      "bleurt score         -0.843204\n",
      "F1 score              0.124287\n",
      "Name: mean, dtype: float64 \n",
      "\n",
      "\n",
      "SuperKnowa_Coqa_flanT5 \n",
      " blue score            0.093683\n",
      "meteor score          0.318327\n",
      "rouge score           0.382027\n",
      "SentenceSim score     0.616914\n",
      "SimHash score        23.842000\n",
      "perplexity score     20.313163\n",
      "bleurt score         -0.400201\n",
      "F1 score              0.340996\n",
      "Name: mean, dtype: float64 \n",
      "\n",
      "\n",
      "SuperKnowa_Coqa_Llama \n",
      " blue score            0.016324\n",
      "meteor score          0.119041\n",
      "rouge score           0.203716\n",
      "SentenceSim score     0.484394\n",
      "SimHash score        26.096000\n",
      "perplexity score     21.272829\n",
      "bleurt score         -0.929731\n",
      "F1 score              0.175145\n",
      "Name: mean, dtype: float64 \n",
      "\n",
      "\n",
      "SuperKnowa_Coqa_bloom \n",
      " blue score            0.018687\n",
      "meteor score          0.088058\n",
      "rouge score           0.141556\n",
      "SentenceSim score     0.463152\n",
      "SimHash score        26.199000\n",
      "perplexity score     21.126773\n",
      "bleurt score         -0.843204\n",
      "F1 score              0.124287\n",
      "Name: mean, dtype: float64 \n",
      "\n",
      "\n",
      "SuperKnowa_Coqa_flanT5 \n",
      " blue score            0.093683\n",
      "meteor score          0.318327\n",
      "rouge score           0.382027\n",
      "SentenceSim score     0.616914\n",
      "SimHash score        23.842000\n",
      "perplexity score     20.313163\n",
      "bleurt score         -0.400201\n",
      "F1 score              0.340996\n",
      "Name: mean, dtype: float64 \n",
      "\n",
      "\n",
      "SuperKnowa_Coqa_Llama \n",
      " blue score            0.016324\n",
      "meteor score          0.119041\n",
      "rouge score           0.203716\n",
      "SentenceSim score     0.484394\n",
      "SimHash score        26.096000\n",
      "perplexity score     21.272829\n",
      "bleurt score         -0.929731\n",
      "F1 score              0.175145\n",
      "Name: mean, dtype: float64 \n",
      "\n",
      "\n",
      "SuperKnowa_Coqa_bloom \n",
      " blue score            0.018687\n",
      "meteor score          0.088058\n",
      "rouge score           0.141556\n",
      "SentenceSim score     0.463152\n",
      "SimHash score        26.199000\n",
      "perplexity score     21.126773\n",
      "bleurt score         -0.843204\n",
      "F1 score              0.124287\n",
      "Name: mean, dtype: float64 \n",
      "\n",
      "\n",
      "SuperKnowa_Coqa_flanT5 \n",
      " blue score            0.093683\n",
      "meteor score          0.318327\n",
      "rouge score           0.382027\n",
      "SentenceSim score     0.616914\n",
      "SimHash score        23.842000\n",
      "perplexity score     20.313163\n",
      "bleurt score         -0.400201\n",
      "F1 score              0.340996\n",
      "Name: mean, dtype: float64 \n",
      "\n",
      "\n",
      "SuperKnowa_Coqa_Llama \n",
      " blue score            0.016324\n",
      "meteor score          0.119041\n",
      "rouge score           0.203716\n",
      "SentenceSim score     0.484394\n",
      "SimHash score        26.096000\n",
      "perplexity score     21.272829\n",
      "bleurt score         -0.929731\n",
      "F1 score              0.175145\n",
      "Name: mean, dtype: float64 \n",
      "\n",
      "\n",
      "SuperKnowa_Coqa_bloom \n",
      " blue score            0.018687\n",
      "meteor score          0.088058\n",
      "rouge score           0.141556\n",
      "SentenceSim score     0.463152\n",
      "SimHash score        26.199000\n",
      "perplexity score     21.126773\n",
      "bleurt score         -0.843204\n",
      "F1 score              0.124287\n",
      "Name: mean, dtype: float64 \n",
      "\n",
      "\n",
      "SuperKnowa_Coqa_flanT5 \n",
      " blue score            0.093683\n",
      "meteor score          0.318327\n",
      "rouge score           0.382027\n",
      "SentenceSim score     0.616914\n",
      "SimHash score        23.842000\n",
      "perplexity score     20.313163\n",
      "bleurt score         -0.400201\n",
      "F1 score              0.340996\n",
      "Name: mean, dtype: float64 \n",
      "\n",
      "\n",
      "SuperKnowa_Coqa_Llama \n",
      " blue score            0.016324\n",
      "meteor score          0.119041\n",
      "rouge score           0.203716\n",
      "SentenceSim score     0.484394\n",
      "SimHash score        26.096000\n",
      "perplexity score     21.272829\n",
      "bleurt score         -0.929731\n",
      "F1 score              0.175145\n",
      "Name: mean, dtype: float64 \n",
      "\n",
      "\n",
      "SuperKnowa_Coqa_bloom \n",
      " blue score            0.018687\n",
      "meteor score          0.088058\n",
      "rouge score           0.141556\n",
      "SentenceSim score     0.463152\n",
      "SimHash score        26.199000\n",
      "perplexity score     21.126773\n",
      "bleurt score         -0.843204\n",
      "F1 score              0.124287\n",
      "Name: mean, dtype: float64 \n",
      "\n",
      "\n",
      "SuperKnowa_Coqa_flanT5 \n",
      " blue score            0.093683\n",
      "meteor score          0.318327\n",
      "rouge score           0.382027\n",
      "SentenceSim score     0.616914\n",
      "SimHash score        23.842000\n",
      "perplexity score     20.313163\n",
      "bleurt score         -0.400201\n",
      "F1 score              0.340996\n",
      "Name: mean, dtype: float64 \n",
      "\n",
      "\n",
      "SuperKnowa_Coqa_Llama \n",
      " blue score            0.016324\n",
      "meteor score          0.119041\n",
      "rouge score           0.203716\n",
      "SentenceSim score     0.484394\n",
      "SimHash score        26.096000\n",
      "perplexity score     21.272829\n",
      "bleurt score         -0.929731\n",
      "F1 score              0.175145\n",
      "Name: mean, dtype: float64 \n",
      "\n",
      "\n",
      "SuperKnowa_Coqa_bloom \n",
      " blue score            0.018687\n",
      "meteor score          0.088058\n",
      "rouge score           0.141556\n",
      "SentenceSim score     0.463152\n",
      "SimHash score        26.199000\n",
      "perplexity score     21.126773\n",
      "bleurt score         -0.843204\n",
      "F1 score              0.124287\n",
      "Name: mean, dtype: float64 \n",
      "\n",
      "\n",
      "SuperKnowa_Coqa_flanT5 \n",
      " blue score            0.093683\n",
      "meteor score          0.318327\n",
      "rouge score           0.382027\n",
      "SentenceSim score     0.616914\n",
      "SimHash score        23.842000\n",
      "perplexity score     20.313163\n",
      "bleurt score         -0.400201\n",
      "F1 score              0.340996\n",
      "Name: mean, dtype: float64 \n",
      "\n",
      "\n",
      "SuperKnowa_Coqa_Llama \n",
      " blue score            0.016324\n",
      "meteor score          0.119041\n",
      "rouge score           0.203716\n",
      "SentenceSim score     0.484394\n",
      "SimHash score        26.096000\n",
      "perplexity score     21.272829\n",
      "bleurt score         -0.929731\n",
      "F1 score              0.175145\n",
      "Name: mean, dtype: float64 \n",
      "\n",
      "\n",
      "SuperKnowa_Coqa_bloom \n",
      " blue score            0.018687\n",
      "meteor score          0.088058\n",
      "rouge score           0.141556\n",
      "SentenceSim score     0.463152\n",
      "SimHash score        26.199000\n",
      "perplexity score     21.126773\n",
      "bleurt score         -0.843204\n",
      "F1 score              0.124287\n",
      "Name: mean, dtype: float64 \n",
      "\n",
      "\n",
      "SuperKnowa_Coqa_flanT5 \n",
      " blue score            0.093683\n",
      "meteor score          0.318327\n",
      "rouge score           0.382027\n",
      "SentenceSim score     0.616914\n",
      "SimHash score        23.842000\n",
      "perplexity score     20.313163\n",
      "bleurt score         -0.400201\n",
      "F1 score              0.340996\n",
      "Name: mean, dtype: float64 \n",
      "\n",
      "\n",
      "SuperKnowa_Coqa_Llama \n",
      " blue score            0.016324\n",
      "meteor score          0.119041\n",
      "rouge score           0.203716\n",
      "SentenceSim score     0.484394\n",
      "SimHash score        26.096000\n",
      "perplexity score     21.272829\n",
      "bleurt score         -0.929731\n",
      "F1 score              0.175145\n",
      "Name: mean, dtype: float64 \n",
      "\n",
      "\n",
      "SuperKnowa_Coqa_bloom \n",
      " blue score            0.018687\n",
      "meteor score          0.088058\n",
      "rouge score           0.141556\n",
      "SentenceSim score     0.463152\n",
      "SimHash score        26.199000\n",
      "perplexity score     21.126773\n",
      "bleurt score         -0.843204\n",
      "F1 score              0.124287\n",
      "Name: mean, dtype: float64 \n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Iterate over the scores and create subplots\n",
    "for i, score in enumerate(scores):\n",
    "    averages = []\n",
    "    for df, file in zip(dfs, csv_files):\n",
    "        model_name = file.split(\".\")[0]\n",
    "        print(model_name,\"\\n\",df.describe().loc['mean'],\"\\n\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "93a31d64",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Name  Blue Score  Meteor Score  Rouge Score  SentenceSim Score  SimHash Score  Perplexity Score  Bleurt Score  F1 Score\n",
      "    flanT5    0.093683      0.318327     0.382027           0.616914         23.842         20.313163     -0.400201  0.340996\n",
      "     Llama    0.016324      0.119041     0.203716           0.484394         26.096         21.272829     -0.929731  0.175145\n",
      "     bloom    0.018687      0.088058     0.141556           0.463152         26.199         21.126773     -0.843204  0.124287\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/2q/zbl1dvln0yq_5wjqjkhf54kw0000gn/T/ipykernel_12556/2638296987.py:18: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  leaderboard = leaderboard.append({'Model Name': model_name,\n",
      "/var/folders/2q/zbl1dvln0yq_5wjqjkhf54kw0000gn/T/ipykernel_12556/2638296987.py:18: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  leaderboard = leaderboard.append({'Model Name': model_name,\n",
      "/var/folders/2q/zbl1dvln0yq_5wjqjkhf54kw0000gn/T/ipykernel_12556/2638296987.py:18: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  leaderboard = leaderboard.append({'Model Name': model_name,\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# List of file names\n",
    "csv_files = ['SuperKnowa_Coqa_flanT5.csv', 'SuperKnowa_Coqa_Llama.csv', 'SuperKnowa_Coqa_bloom.csv']\n",
    "\n",
    "# Substring to remove from model names\n",
    "substring_to_remove = 'SuperKnowa_Coqa_'\n",
    "\n",
    "# Create an empty DataFrame\n",
    "leaderboard = pd.DataFrame(columns=['Model Name', 'Blue Score', 'Meteor Score', 'Rouge Score', 'SentenceSim Score', 'SimHash Score', 'Perplexity Score', 'Bleurt Score', 'F1 Score'])\n",
    "\n",
    "# Iterate over the scores files\n",
    "for file in csv_files:\n",
    "    model_name = file.split(\".\")[0]\n",
    "    model_name = model_name.replace(substring_to_remove, '')  # Remove the specified substring\n",
    "    df = pd.read_csv(file)  # Assuming the scores are stored in CSV format\n",
    "    mean_scores = df.describe().loc['mean']\n",
    "    leaderboard = leaderboard.append({'Model Name': model_name,\n",
    "                                      'Blue Score': mean_scores['blue score'],\n",
    "                                      'Meteor Score': mean_scores['meteor score'],\n",
    "                                      'Rouge Score': mean_scores['rouge score'],\n",
    "                                      'SentenceSim Score': mean_scores['SentenceSim score'],\n",
    "                                      'SimHash Score': mean_scores['SimHash score'],\n",
    "                                      'Perplexity Score': mean_scores['perplexity score'],\n",
    "                                      'Bleurt Score': mean_scores['bleurt score'],\n",
    "                                      'F1 Score': mean_scores['F1 score']}, ignore_index=True)\n",
    "\n",
    "# Print the leaderboard\n",
    "print(leaderboard.to_string(index=False))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5655cb88",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2bfe2916",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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